Most productivity monitoring software collects:
A smaller number of tools have more invasive monitoring features like keystroke logging, screen recording, or message content. That said, teams that need or want these features are relatively uncommon.
If you’re evaluating a productivity monitoring tool right now, you might be thinking about what it collects from your team. When does monitoring become too much? What separates a tool that helps improve productivity from one designed to watch each employee’s every move?
The same questions apply if you are an employee who has just learned your employer uses such a tool.
For most rollouts, teams aren’t trying to surveil their members. Instead, they’re trying to stop losing hours guessing why work is (or isn’t) getting done.
Few people ask this question because they’re simply curious about the software category in abstract. Often, they are decision-makers who want to understand the precise level of monitoring their team needs.
These decision-makers also want to know how to avoid monitoring more than they need. For most of them, they decided they needed to start monitoring because:
Very rarely does someone implement monitoring because they have an appetite for surveillance.
There’s no list of arbitrary things a monitoring tool must be able to track for it to fit in that software category. Tools can range anywhere from doing what punch clocks did to watching everything you can see on a screen.
That said, most productivity monitoring tools can track the following data.
The most basic layer, and the thing almost every tool does, is keeping track of when somebody worked.
Time and attendance metrics entail essentially zero controversy because, at its core, time tracking is just a more effortless and more precise version of punch clocks. Here’s what time and attendance data typically looks like:
Virtually every productivity monitoring platform builds its tracking capabilities with time tracking as the foundation.
This type of data is significantly more detailed than time and attendance data. Tools with time and activity tracking sit very far still from the surveillance end of the spectrum, but it can understandably make teams nervous because many conversations about productivity percentages stem from this metric.
Here are a few common examples of activity data:
Activity levels, a standard metric among several productivity monitoring tools, are often misunderstood. This metric measures whether there is keyboard or mouse movement. It does not track keystroke content.
Activity data is very contextual and should be treated as such. As powerful as this data can be when used properly, it is simply impossible to distill a person’s work and all its nuances into a single number. As such, activity data should not be used as a standalone measure of employee performance.
As a simple example, someone on sales calls for four hours a day will always have lower activity scores than someone performing data entry in the same amount of time. The person performing sales is also far more likely to be flagged with more idle time despite putting in real work.
Whereas activity tracking focuses on how much movement was made by the employee, this category looks at which applications or tools had focus, which websites employees visited when they were working, and for how long.
This category usually covers:
Notably, the last one is newer compared to the other bullet points. AI usage is something a lot of companies are thinking about right now, which is why many tools out there have been updated to detect when and how teams are using AI tools.
Unlike keyboard and mouse activity metrics, app and website data don't require any guesswork. Teams can easily the activities employees spend time on.
One of the tools that tracks these metrics well is Hubstaff. It can not only track web and app data but also classify activity as productive or non-productive. This is a powerful feature because one site or tool may be productive to one person, but a distraction to another.
The web and app layer of data is the most flexible. This is where you’ll find capabilities like role-based rules, dedicated categories for AI tool usage, and the option to let employees see their own data the same way a manager would.
Conversely, it is also the one that requires the most care in terms of configuration to avoid holding employees to standards that shouldn’t apply to them.
Some industries need screenshots for compliance purposes, and that's the strongest use case behind this feature.
For a lot of tools, screenshots are disabled by default. It's a feature that's easy to misuse, not because anyone building it intends to, but because a screenshot doesn't know the difference between a work document and whatever else happened to be on the screen at that moment.
Time trackers with screenshots are excellent to have in an industry where compliance is strict. Outside of that, it requires more discretion than most of the other data we’ve covered so far, because a screenshot can catch things it wasn’t supposed to.
Good tools build that discretion in. You’ll see this in how tools allow you to configure:
Any tool that can capture screenshots can be used for the wrong reasons. This is why having the ability to blur and delete screenshots says a lot about where a tool stands on the surveillance vs. verification line.
Hubstaff, for instance, helps companies meet compliance requirements with optional screenshot capabilities, but it does so while giving employees controls to protect their privacy.
Hubstaff can be set to take as many as three screenshots at random every 10 minutes, across multiple monitors, on desktop apps. More importantly, team members can blur or delete screenshots if screenshots of non-work-related or confidential data were taken.
This is where all the earlier data — time, activity, apps, and screenshots — become more than the sum of its parts.
Instead of zeroing in on one point in time or a single metric, the workforce analytics looks at big-picture patterns across weeks, teams, and even industries, to clearly understand how work is happening inside the organization.
This category includes workforce analytics metrics like:
At this point, teams get the data they need to perform not only activity monitoring but also effective workforce planning. They’ll be able to identify where capacity may be running thin, if any team members are overloaded, and if there is anything holding back a team that isn’t performing as well as it can be.
The honest answer depends on the tool. Below is what most invasive software can generally do, and what transparency-first tools deliberately avoid doing at all.
Some tools log every keystroke. Others, Hubstaff among them, do not. They measure keyboard and mouse movement, not what the user is typing. There's an important distinction between knowing someone's hands were active and knowing what they typed.
There are tools that can read the actual content. More restrained ones can’t, although email or message contents can be visible if a screenshot capture takes place when a team member is on that specific window. Hubstaff doesn’t monitor email or message content — it can track time spent on platforms like Slack and Gmail, but not the messaging content inside the tools.
Reputable tools don't. Keystroke-logging tools could, in theory, be one more reason teams should be extremely careful before implementing tools with that functionality.
Standard activity or app tracking has no access to file contents. Continuous screen recording is different as it can reveal whatever happens to be on screen at the time, which is why it is important for teams to have the ability to configure recording and screenshot settings.
Surveillance-heavy tools sometimes offer this. Transparency-first tools don't.
Automated tracking does not collect anything different from tracking that someone starts and stops themselves. You get the same activity levels, the same app and website data, and by extension, the same workforce analytics on top of that.
What changes is the manner in which the tracking starts. Instead of somebody manually clicking a timer, the tracker runs on its own because it was set according to a policy beforehand.
Where the automatic tracker runs also makes a difference. Typically, a desktop app carries the full range of features. A browser extension or mobile app often doesn't take screenshots at all, while mobile adds a capability the other platforms don't have, GPS, because a phone is mobile by its very nature.
The primary benefit of such a feature is that it enables the highest levels of accuracy in timesheets and workforce data. A good example of a tool that makes this possible through automated tracking on company-owned devices is Hubstaff.
However, the ability to track automatically in the background requires a lot of transparency from leadership in order to be effective.
When somebody starts their own timer, they know it's running. That's not the case with background tracking.
If they don't know beforehand that they're being automatically tracked at work, there's no way for them to know at all. That’s a scenario that leads to broken trust and defeats the purpose behind the tracking entirely.
The company should tell all team members about the tracking beforehand, in writing, so that they completely understand before the background tracking is implemented. We recommend treating transparency with tracked data like a best practice. Additionally, acquiring employee consent before tracking is a general requirement for several employee monitoring laws.
With information as powerful as productivity monitoring data, access should not be all-or-nothing. A reliable tool should allow organizations to assign visibility according to what a specific role needs, which naturally prevents people from tracking more than necessary.
Here are examples of how roles can be configured within productivity monitoring software:
The general philosophy behind this hierarchy can be adapted to most industries. Each role should see only as much as its real responsibilities require, and the size of that access should grow only when the responsibilities do too.
This approach to fair, reasonable access is a core guiding principle to how we design Hubstaff. Leaders and managers can see only data that their role requires, while team members can always access their own data.
There are advantages and disadvantages to employee monitoring. It can be a powerful tool if used right, but it can just as easily damage workplace trust and harm employee relationships if not.
Particularly, how you use productivity data yields the most palpable results for better or worse. Productivity data should be treated as additional context, not the determining factor in decisions related to managing people.
For instance, if a project fell behind, then the data must be used to better understand why it happened, so the process can be improved, instead of using the data to find a responsible person to blame.
The same activity numbers that look like evidence of a problem can just as easily be evidence of a bad deadline, or a task that was harder than anyone accounted for when the schedule was drawn up. The only way to tell the difference is to have conversations with the people who perform these jobs, which isn’t something any tool can do on your behalf.
Workload balancing shows this clearly too. Time data can tell you who's been putting in long hours and who hasn't, but it doesn't know the story behind those long hours. Maybe someone is overwhelmed, mismatched to the work, or distracted by something outside of work entirely — the data can't tell the difference. It just points at a pattern, and a person has to decide what the pattern means and what to do with it.
Coaching is where this gets the most personal, because anybody can use a number as a verdict on someone's performance. A dip in activity looks the same whether someone is coasting or just doing work that requires more thinking than clicking. The data can't tell you which.
Zoom out to workforce analytics, and nothing changes aside from the scale. You might see a trend here or a spike there, but none of this changes the fact that a person will have to read and act on it.
Everything here starts with communication.
Before you track anything, employees should know what's being collected, how, and why. You need to have a real, human conversation with everyone in the team, and you need to have an extensive written tracking policy. If you don’t, you will leave people to assume the worst, which they usually will.
Permissions matter here, too. Who can see the data should be specific (i.e., real, named roles, not "management"), and that access should map to what each role needs to be successful.
You also need to spell out what retention looks like:
A retention window signals that the data exists for a reason as opposed to indefinitely.
Compliance adds another layer for certain industries. Healthcare organizations often need HIPAA compliance, while enterprise buyers look for SOC 2 or GDPR certifications.
With that said, the use of a certified tool doesn’t make an organization inherently compliant. A tool being certified means it can meet compliance standards, but that would still depend on how the organization configures and uses the tool. Since requirements vary by region and industry, we strongly recommend reviewing the specific laws that apply before rolling anything out.
Organizations do not collect productivity data simply to monitor activity. Besides, having powerful productivity data at your fingertips would be wasteful if you only use it to watch people work.
The goal, instead, is to transform operational data into insights that improve workforce performance, productivity, and decision-making.
Productivity monitoring tools give you the means to collect that data, but that data’s real value appears when it’s scaled to an organizational level, where it can help leaders make decisions about resource planning, team capacity, and getting team performance where it needs to be to sustain long-term growth.